SummaryI propose to investigate the nanometer scale organization of delicate 2-dimensional molecular materials using nanoscale vibrational spectroscopy. 2D structures are of great scientific and technological importance, for example as novel materials (graphene, MoS2, WS2, etc.), and in the form of biological membranes and synthetic 2D-polymers. Powerful methods for their analysis and imaging with molecular selectivity and sufficient spatial resolution, however, are lacking. Tip-enhanced Raman spectroscopy (TERS) allows label-free spectroscopic identification of molecular species, with ≈10 nm spatial resolution, and with single molecule sensitivity for strong Raman scatterers. So far, however, TERS is not being carried out in liquids, which is the natural environment for membranes, and its application to poor Raman scatterers such as components of 2D polymers, lipids, or other membrane compounds (proteins, sugars) is difficult. TERS has the potential to overcome the restrictions of other optical/spectroscopic methods to study 2D materials, namely (i) insufficient spatial resolution of diffraction-limited optical methods; (ii) the need for labelling for all methods relying on fluorescence; and (iii) the inability of some methods to work in liquids. I propose to address a number of scientific questions associated with the spatial organization, and the occurrence of defects in sensitive 2D molecular materials. The success of these studies will also rely critically on technical innovations of TERS that notably address the problem of energy dissipation. This will for the first time allow its application to study of complex, delicate 2D molecular systems without photochemical damage.

I propose to investigate the nanometer scale organization of delicate 2-dimensional molecular materials using nanoscale vibrational spectroscopy. 2D structures are of great scientific and technological importance, for example as novel materials (graphene, MoS2, WS2, etc.), and in the form of biological membranes and synthetic 2D-polymers. Powerful methods for their analysis and imaging with molecular selectivity and sufficient spatial resolution, however, are lacking. Tip-enhanced Raman spectroscopy (TERS) allows label-free spectroscopic identification of molecular species, with ≈10 nm spatial resolution, and with single molecule sensitivity for strong Raman scatterers. So far, however, TERS is not being carried out in liquids, which is the natural environment for membranes, and its application to poor Raman scatterers such as components of 2D polymers, lipids, or other membrane compounds (proteins, sugars) is difficult. TERS has the potential to overcome the restrictions of other optical/spectroscopic methods to study 2D materials, namely (i) insufficient spatial resolution of diffraction-limited optical methods; (ii) the need for labelling for all methods relying on fluorescence; and (iii) the inability of some methods to work in liquids. I propose to address a number of scientific questions associated with the spatial organization, and the occurrence of defects in sensitive 2D molecular materials. The success of these studies will also rely critically on technical innovations of TERS that notably address the problem of energy dissipation. This will for the first time allow its application to study of complex, delicate 2D molecular systems without photochemical damage.

Max ERC Funding

2 311 696 €

Duration

Start date: 2017-09-01, End date: 2022-08-31

Project acronym5HT-OPTOGENETICS

ProjectOptogenetic Analysis of Serotonin Function in the Mammalian Brain

SummarySerotonin (5-HT) is implicated in a wide spectrum of brain functions and disorders. However, its functions remain controversial and enigmatic. We suggest that past work on the 5-HT system have been significantly hampered by technical limitations in the selectivity and temporal resolution of the conventional pharmacological and electrophysiological methods that have been applied. We therefore propose to apply novel optogenetic methods that will allow us to overcome these limitations and thereby gain new insight into the biological functions of this important molecule. In preliminary studies, we have demonstrated that we can deliver exogenous proteins specifically to 5-HT neurons using viral vectors. Our objectives are to (1) record, (2) stimulate and (3) silence the activity of 5-HT neurons with high molecular selectivity and temporal precision by using genetically-encoded sensors, activators and inhibitors of neural function. These tools will allow us to monitor and control the 5-HT system in real-time in freely-behaving animals and thereby to establish causal links between information processing in 5-HT neurons and specific behaviors. In combination with quantitative behavioral assays, we will use this approach to define the role of 5-HT in sensory, motor and cognitive functions. The significance of the work is three-fold. First, we will establish a new arsenal of tools for probing the physiological and behavioral functions of 5-HT neurons. Second, we will make definitive tests of major hypotheses of 5-HT function. Third, we will have possible therapeutic applications. In this way, the proposed work has the potential for a major impact in research on the role of 5-HT in brain function and dysfunction.

Serotonin (5-HT) is implicated in a wide spectrum of brain functions and disorders. However, its functions remain controversial and enigmatic. We suggest that past work on the 5-HT system have been significantly hampered by technical limitations in the selectivity and temporal resolution of the conventional pharmacological and electrophysiological methods that have been applied. We therefore propose to apply novel optogenetic methods that will allow us to overcome these limitations and thereby gain new insight into the biological functions of this important molecule. In preliminary studies, we have demonstrated that we can deliver exogenous proteins specifically to 5-HT neurons using viral vectors. Our objectives are to (1) record, (2) stimulate and (3) silence the activity of 5-HT neurons with high molecular selectivity and temporal precision by using genetically-encoded sensors, activators and inhibitors of neural function. These tools will allow us to monitor and control the 5-HT system in real-time in freely-behaving animals and thereby to establish causal links between information processing in 5-HT neurons and specific behaviors. In combination with quantitative behavioral assays, we will use this approach to define the role of 5-HT in sensory, motor and cognitive functions. The significance of the work is three-fold. First, we will establish a new arsenal of tools for probing the physiological and behavioral functions of 5-HT neurons. Second, we will make definitive tests of major hypotheses of 5-HT function. Third, we will have possible therapeutic applications. In this way, the proposed work has the potential for a major impact in research on the role of 5-HT in brain function and dysfunction.

Max ERC Funding

2 318 636 €

Duration

Start date: 2010-07-01, End date: 2015-12-31

Project acronym5HTCircuits

ProjectModulation of cortical circuits and predictive neural coding by serotonin

SummarySerotonin (5-HT) is a central neuromodulator and a major target of therapeutic psychoactive drugs, but relatively little is known about how it modulates information processing in neural circuits. The theory of predictive coding postulates that the brain combines raw bottom-up sensory information with top-down information from internal models to make perceptual inferences about the world. We hypothesize, based on preliminary data and prior literature, that a role of 5-HT in this process is to report prediction errors and promote the suppression and weakening of erroneous internal models. We propose that it does this by inhibiting top-down relative to bottom-up cortical information flow. To test this hypothesis, we propose a set of experiments in mice performing olfactory perceptual tasks. Our specific aims are: (1) We will test whether 5-HT neurons encode sensory prediction errors. (2) We will test their causal role in using predictive cues to guide perceptual decisions. (3) We will characterize how 5-HT influences the encoding of sensory information by neuronal populations in the olfactory cortex and identify the underlying circuitry. (4) Finally, we will map the effects of 5-HT across the whole brain and use this information to target further causal manipulations to specific 5-HT projections. We accomplish these aims using state-of-the-art optogenetic, electrophysiological and imaging techniques (including 9.4T small-animal functional magnetic resonance imaging) as well as psychophysical tasks amenable to quantitative analysis and computational theory. Together, these experiments will tackle multiple facets of an important general computational question, bringing to bear an array of cutting-edge technologies to address with unprecedented mechanistic detail how 5-HT impacts neural coding and perceptual decision-making.

Serotonin (5-HT) is a central neuromodulator and a major target of therapeutic psychoactive drugs, but relatively little is known about how it modulates information processing in neural circuits. The theory of predictive coding postulates that the brain combines raw bottom-up sensory information with top-down information from internal models to make perceptual inferences about the world. We hypothesize, based on preliminary data and prior literature, that a role of 5-HT in this process is to report prediction errors and promote the suppression and weakening of erroneous internal models. We propose that it does this by inhibiting top-down relative to bottom-up cortical information flow. To test this hypothesis, we propose a set of experiments in mice performing olfactory perceptual tasks. Our specific aims are: (1) We will test whether 5-HT neurons encode sensory prediction errors. (2) We will test their causal role in using predictive cues to guide perceptual decisions. (3) We will characterize how 5-HT influences the encoding of sensory information by neuronal populations in the olfactory cortex and identify the underlying circuitry. (4) Finally, we will map the effects of 5-HT across the whole brain and use this information to target further causal manipulations to specific 5-HT projections. We accomplish these aims using state-of-the-art optogenetic, electrophysiological and imaging techniques (including 9.4T small-animal functional magnetic resonance imaging) as well as psychophysical tasks amenable to quantitative analysis and computational theory. Together, these experiments will tackle multiple facets of an important general computational question, bringing to bear an array of cutting-edge technologies to address with unprecedented mechanistic detail how 5-HT impacts neural coding and perceptual decision-making.

SummaryQuantum chemistry provides two approaches to molecular electronic-structure calculations: the systematically refinable but expensive many-body wave-function methods and the inexpensive but not systematically refinable Kohn Sham method of density-functional theory (DFT). The accuracy of Kohn Sham calculations is determined by the quality of the exchange correlation functional, from which the effects of exchange and correlation among the electrons are extracted using the density rather than the wave function. However, the exact exchange correlation functional is unknown—instead, many approximate forms have been developed, by fitting to experimental data or by satisfying exact relations. Here, a new approach to density-functional analysis and construction is proposed: the Lieb variation principle, usually regarded as conceptually important but impracticable. By invoking the Lieb principle, it becomes possible to approach the development of approximate functionals in a novel manner, being directly guided by the behaviour of exact functional, accurately calculated for a wide variety of chemical systems. In particular, this principle will be used to calculate ab-initio adiabatic connection curves, studying the exchange correlation functional for a fixed density as the electronic interactions are turned on from zero to one. Pilot calculations have indicated the feasibility of this approach in simple cases—here, a comprehensive set of adiabatic-connection curves will be generated and utilized for calibration, construction, and analysis of density functionals, the objective being to produce improved functionals for Kohn Sham calculations by modelling or fitting such curves. The ABACUS approach will be particularly important in cases where little experimental information is available—for example, for understanding and modelling the behaviour of the exchange correlation functional in electromagnetic fields.

Quantum chemistry provides two approaches to molecular electronic-structure calculations: the systematically refinable but expensive many-body wave-function methods and the inexpensive but not systematically refinable Kohn Sham method of density-functional theory (DFT). The accuracy of Kohn Sham calculations is determined by the quality of the exchange correlation functional, from which the effects of exchange and correlation among the electrons are extracted using the density rather than the wave function. However, the exact exchange correlation functional is unknown—instead, many approximate forms have been developed, by fitting to experimental data or by satisfying exact relations. Here, a new approach to density-functional analysis and construction is proposed: the Lieb variation principle, usually regarded as conceptually important but impracticable. By invoking the Lieb principle, it becomes possible to approach the development of approximate functionals in a novel manner, being directly guided by the behaviour of exact functional, accurately calculated for a wide variety of chemical systems. In particular, this principle will be used to calculate ab-initio adiabatic connection curves, studying the exchange correlation functional for a fixed density as the electronic interactions are turned on from zero to one. Pilot calculations have indicated the feasibility of this approach in simple cases—here, a comprehensive set of adiabatic-connection curves will be generated and utilized for calibration, construction, and analysis of density functionals, the objective being to produce improved functionals for Kohn Sham calculations by modelling or fitting such curves. The ABACUS approach will be particularly important in cases where little experimental information is available—for example, for understanding and modelling the behaviour of the exchange correlation functional in electromagnetic fields.

Max ERC Funding

2 017 932 €

Duration

Start date: 2011-03-01, End date: 2016-02-29

Project acronymACCELERATES

ProjectAcceleration in Extreme Shocks: from the microphysics to laboratory and astrophysics scenarios

Researcher (PI)Luis Miguel De Oliveira E Silva

Host Institution (HI)INSTITUTO SUPERIOR TECNICO

Call DetailsAdvanced Grant (AdG), PE2, ERC-2010-AdG_20100224

SummaryWhat is the origin of cosmic rays, what are the dominant acceleration mechanisms in relativistic shocks, how do cosmic rays self-consistently influence the shock dynamics, how are relativistic collisionless shocks formed are longstanding scientific questions, closely tied to extreme plasma physics processes, and where a close interplay between the micro-instabilities and the global dynamics is critical.
Relativistic shocks are closely connected with the propagation of intense streams of particles pervasive in many astrophysical scenarios. The possibility of exciting shocks in the laboratory will also be available very soon with multi-PW lasers or intense relativistic particle beams.
Computational modeling is now established as a prominent research tool, by enabling the fully kinetic modeling of these systems for the first time. With the fast paced developments in high performance computing, the time is ripe for a focused research programme on simulation-based studies of relativistic shocks. This proposal therefore focuses on using self-consistent ab initio massively parallel simulations to study the physics of relativistic shocks, bridging the gap between the multidimensional microphysics of shock onset, formation, and propagation and the global system dynamics. Particular focus will be given to the shock acceleration mechanisms and the radiation signatures of the various physical processes, with the goal of solving some of the central questions in plasma/relativistic phenomena in astrophysics and in the laboratory, and opening new avenues between theoretical/massive computational studies, laboratory experiments and astrophysical observations.

What is the origin of cosmic rays, what are the dominant acceleration mechanisms in relativistic shocks, how do cosmic rays self-consistently influence the shock dynamics, how are relativistic collisionless shocks formed are longstanding scientific questions, closely tied to extreme plasma physics processes, and where a close interplay between the micro-instabilities and the global dynamics is critical.
Relativistic shocks are closely connected with the propagation of intense streams of particles pervasive in many astrophysical scenarios. The possibility of exciting shocks in the laboratory will also be available very soon with multi-PW lasers or intense relativistic particle beams.
Computational modeling is now established as a prominent research tool, by enabling the fully kinetic modeling of these systems for the first time. With the fast paced developments in high performance computing, the time is ripe for a focused research programme on simulation-based studies of relativistic shocks. This proposal therefore focuses on using self-consistent ab initio massively parallel simulations to study the physics of relativistic shocks, bridging the gap between the multidimensional microphysics of shock onset, formation, and propagation and the global system dynamics. Particular focus will be given to the shock acceleration mechanisms and the radiation signatures of the various physical processes, with the goal of solving some of the central questions in plasma/relativistic phenomena in astrophysics and in the laboratory, and opening new avenues between theoretical/massive computational studies, laboratory experiments and astrophysical observations.

Max ERC Funding

1 588 800 €

Duration

Start date: 2011-06-01, End date: 2016-07-31

Project acronymAGNOSTIC

ProjectActively Enhanced Cognition based Framework for Design of Complex Systems

Researcher (PI)Björn Ottersten

Host Institution (HI)UNIVERSITE DU LUXEMBOURG

Call DetailsAdvanced Grant (AdG), PE7, ERC-2016-ADG

SummaryParameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.

Parameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.

SummaryPlants typically release large quantities of volatiles in response to attack by herbivores or pathogens. I may claim to have contributed to various breakthroughs in this research field, including the discovery that the volatile blends induced by different attackers are astonishingly specific, resulting in characteristic, readily distinguishable odour blends. Using maize as our model plant, I wish to take several leaps forward in our understanding of this signal specificity and use this knowledge to develop sensors for the real-time detection of crop pests and diseases. For this, three interconnected work-packages will aim to:
• Develop chemical analytical techniques and statistical models to decipher the odorous vocabulary of plants, and to create a complete inventory of “odour-prints” for a wide range of herbivore-plant and pathogen-plant combinations, including simultaneous infestations.
• Develop and optimize nano-mechanical sensors for the detection of specific plant volatile mixtures. For this, we will initially adapt a prototype sensor that has been successfully developed for the detection of cancer-related volatiles in human breath.
• Genetically manipulate maize plants to release a unique blend of root-produced volatiles upon herbivory. For this, we will engineer gene cassettes that combine recently identified P450 (CYP) genes from poplar with inducible, root-specific promoters from maize. This will result in maize plants that, in response to pest attack, release easy-to-detect aldoximes and nitriles from their roots.
In short, by investigating and manipulating the specificity of inducible odour blends we will generate the necessary knowhow to develop a novel odour-detection device. The envisioned sensor technology will permit real-time monitoring of the pests and enable farmers to apply crop protection treatments at the right time and in the right place.

Plants typically release large quantities of volatiles in response to attack by herbivores or pathogens. I may claim to have contributed to various breakthroughs in this research field, including the discovery that the volatile blends induced by different attackers are astonishingly specific, resulting in characteristic, readily distinguishable odour blends. Using maize as our model plant, I wish to take several leaps forward in our understanding of this signal specificity and use this knowledge to develop sensors for the real-time detection of crop pests and diseases. For this, three interconnected work-packages will aim to:
• Develop chemical analytical techniques and statistical models to decipher the odorous vocabulary of plants, and to create a complete inventory of “odour-prints” for a wide range of herbivore-plant and pathogen-plant combinations, including simultaneous infestations.
• Develop and optimize nano-mechanical sensors for the detection of specific plant volatile mixtures. For this, we will initially adapt a prototype sensor that has been successfully developed for the detection of cancer-related volatiles in human breath.
• Genetically manipulate maize plants to release a unique blend of root-produced volatiles upon herbivory. For this, we will engineer gene cassettes that combine recently identified P450 (CYP) genes from poplar with inducible, root-specific promoters from maize. This will result in maize plants that, in response to pest attack, release easy-to-detect aldoximes and nitriles from their roots.
In short, by investigating and manipulating the specificity of inducible odour blends we will generate the necessary knowhow to develop a novel odour-detection device. The envisioned sensor technology will permit real-time monitoring of the pests and enable farmers to apply crop protection treatments at the right time and in the right place.

SummaryRecurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our supervised Long Short-Term Memory (LSTM) RNNs were the first to win pattern recognition contests, and recently enabled best known results in speech and handwriting recognition, machine translation, etc. They are now available to billions of users through the world's most valuable public companies including Google and Apple. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn important types of algorithms. This ERC project will go far beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics: (1) How can RNNs learn to control (through internal spotlights of attention) separate large short-memory structures such as sub-networks with fast weights, to improve performance on many natural short-term memory-intensive tasks which are currently hard to learn by RNNs, such as answering detailed questions on recently observed videos? (2) How can such RNN-like systems metalearn entire learning algorithms that outperform the original learning algorithms? (3) How to achieve efficient transfer learning from one RNN-learned set of problem-solving programs to new RNN programs solving new tasks? In other words, how can one RNN-like system actively learn to exploit algorithmic information contained in the programs running on another? We will test our systems existing benchmarks, and create new, more challenging multi-task benchmarks. This will be supported by a rather cheap, GPU-based mini-brain for implementing large RNNs.

Recurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our supervised Long Short-Term Memory (LSTM) RNNs were the first to win pattern recognition contests, and recently enabled best known results in speech and handwriting recognition, machine translation, etc. They are now available to billions of users through the world's most valuable public companies including Google and Apple. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn important types of algorithms. This ERC project will go far beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics: (1) How can RNNs learn to control (through internal spotlights of attention) separate large short-memory structures such as sub-networks with fast weights, to improve performance on many natural short-term memory-intensive tasks which are currently hard to learn by RNNs, such as answering detailed questions on recently observed videos? (2) How can such RNN-like systems metalearn entire learning algorithms that outperform the original learning algorithms? (3) How to achieve efficient transfer learning from one RNN-learned set of problem-solving programs to new RNN programs solving new tasks? In other words, how can one RNN-like system actively learn to exploit algorithmic information contained in the programs running on another? We will test our systems existing benchmarks, and create new, more challenging multi-task benchmarks. This will be supported by a rather cheap, GPU-based mini-brain for implementing large RNNs.

SummaryThe project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.

The project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.

SummaryCalcium-activated chloride channels (CaCCs) play key roles in a range of physiological processes such as the control of membrane excitability, photoreception and epithelial secretion. Although the importance of these channels has been recognized for more than 30 years their molecular identity remained obscure. The recent discovery of two protein families encoding for CaCCs, Anoctamins and Bestrophins, was a scientific breakthrough that has provided first insight into two novel ion channel architectures. Within this proposal we aim to determine the first high resolution structures of members of both families and study their functional behavior by an interdisciplinary approach combining biochemistry, X-ray crystallography and electrophysiology. The structural investigation of eukaryotic membrane proteins is extremely challenging and will require us to investigate large numbers of candidates to single out family members with superior biochemical properties. During the last year we have made large progress in this direction. By screening numerous eukaryotic Anoctamins and prokaryotic Bestrophins we have identified well-behaved proteins for both families, which were successfully scaled-up and purified. Additional family members will be identified within the course of the project. For these stable proteins we plan to grow crystals diffracting to high resolution and to proceed with structure determination. With first structural information in hand we will perform detailed functional studies using electrophysiology and complementary biophysical techniques to gain mechanistic insight into ion permeation and gating. As the pharmacology of both families is still in its infancy we will in later stages also engage in the identification and characterization of inhibitors and activators of Anoctamins and Bestrophins to open up a field that may ultimately lead to the discovery of novel therapeutic strategies targeting calcium-activated chloride channels.

Calcium-activated chloride channels (CaCCs) play key roles in a range of physiological processes such as the control of membrane excitability, photoreception and epithelial secretion. Although the importance of these channels has been recognized for more than 30 years their molecular identity remained obscure. The recent discovery of two protein families encoding for CaCCs, Anoctamins and Bestrophins, was a scientific breakthrough that has provided first insight into two novel ion channel architectures. Within this proposal we aim to determine the first high resolution structures of members of both families and study their functional behavior by an interdisciplinary approach combining biochemistry, X-ray crystallography and electrophysiology. The structural investigation of eukaryotic membrane proteins is extremely challenging and will require us to investigate large numbers of candidates to single out family members with superior biochemical properties. During the last year we have made large progress in this direction. By screening numerous eukaryotic Anoctamins and prokaryotic Bestrophins we have identified well-behaved proteins for both families, which were successfully scaled-up and purified. Additional family members will be identified within the course of the project. For these stable proteins we plan to grow crystals diffracting to high resolution and to proceed with structure determination. With first structural information in hand we will perform detailed functional studies using electrophysiology and complementary biophysical techniques to gain mechanistic insight into ion permeation and gating. As the pharmacology of both families is still in its infancy we will in later stages also engage in the identification and characterization of inhibitors and activators of Anoctamins and Bestrophins to open up a field that may ultimately lead to the discovery of novel therapeutic strategies targeting calcium-activated chloride channels.